AI and Cancer: Building data-driven computer models of mini-tumours

3rd Supervisor: Dr Theodore Kypraios, University of Nottingham

Organoids are miniature laboratory-grown organs that mimic the properties of the tissue type from which they are derived. In cancer, organoids or “mini-tumours” can be grown from a sample of a patient’s tumour and used to investigate how different treatments might affect the tumour without affecting the patient directly. Measurements can be taken on organoids using a variety of state-of-the-art ‘omics technologies that enable us to measure changes in DNA, RNA and epigenetics right down to single-cell resolution in a high-throughput fashion. Despite recent experimental advances, the construction of mini-tumour models and experimentation is still a laborious and expensive activity.

This PhD project aims to use Artificial Intelligence techniques to construct a state-of-the-art computer simulation model of a mini-tumour. This computer model will enable cancer biologists to conduct in-silico experiments to predict the likely response of a mini-tumour to a novel treatment before it is confirmed with an actual experiment. This will reduce wasted time and effort pursuing experiments that have no positive outcome. Importantly, in the construction of this model, we will also learn more about how cancers function. These models will be trained using high-dimensional experimental data that defy human interpretation and require a machine-based learning system to extract important relationships. Building this computer model will help us to learn the “rules” which govern the behaviour of these mini-tumours and how cancers evolve.

The student will develop in a high-quality research environment where they will develop strong computational and biological knowledge. This project brings together an exciting supervision team comprising of Dr Christopher Yau (Birmingham/Turing, http://cwcyau.github.io), who is a leading Machine Learning researcher and Fellow of the Alan Turing Institute for Artificial Intelligence and Data Science, Dr Andrew Beggs (Birmingham, https://sites.google.com/andrewbeggs.org/beggslab/current-research-projects) whose laboratory is constructing organoid models of colorectal cancer and using single cell sequencing technology to investigate them and Dr Theo Kypraios (Nottingham, https://www.maths.nottingham.ac.uk/personal/tk/) a leading Bayesian Statistician and Chair of the Royal Statistical Society Statistical Computing Section. Industrial support will be provided by Dr Hugo Lam (Roche) whose organisation would be interested in the utility of the model in pharmaceutical research.

For further information please contact Dr Christopher Yau: [Email Address Removed]

Person Specification
Applicants should have a strong background in a mathematical-based subject, and ideally a background in computational science. They should have a commitment to research in Computational Biology and hold or realistically expect to obtain at least an Upper Second Class Honours Degree in Mathematics/Statistics/Computer Science/Physics/Engineering.

To apply please follow the information on the application link https://www.birmingham.ac.uk/schools/mds-graduate-school/scholarships/mrc-impact/index.aspx